The Lane Detection Model Based on Data Augmentation and Deep Learning

Lane detection serves as a cornerstone task in autonomous driving systems, as it directly impacts the vehicles ability to maintain lane discipline, ensure safety, and perform accurate path planning. Although U-Net-based deep learning models have demonstrated strong potential for automatic lane segmentation, their performance can degrade significantly under complex real-world conditions such as variable lighting, occlusions, and worn or curved lane markings.To address these limitations, this study proposes an enhanced lane detection framework built upon the U-Net architecture. The proposed model integrates three key improvements: (1) advanced data augmentation techniques to increase the diversity and robustness of the training data, (2) a refined loss function combining PolyLoss and contrastive loss to address foreground-background imbalance and enhance structural learning, and (3) an optimized upsampling strategy designed to better preserve spatial details and lane continuity in the output predictions.Extensive experiments conducted on the TuSimple lane detection benchmark validate the effectiveness of our approach. The enhanced model achieves an Intersection over Union (IoU) of 44.49%, significantly surpassing the baseline U-Nets performance of 40.36%. These results confirm that the proposed modifications not only improve segmentation accuracy but also enhance the models robustness and generalization capability in real-world driving scenarios. Overall, this work contributes practical insights and techniques that can facilitate the deployment of lane detection systems in intelligent transportation and autonomous vehicle platforms.